Combinatorial Chemistry & High Throughput Screening - Volume 17, Issue 2, 2014

RNAi screening in combination with the genome-sequencing projects would constitute the Holy Grail of modern genetics; enabling discovery and validation towards a better understanding of fundamental biology leading to novel targets to combat disease. Hit discordance at inter-screen level together with the lack of reproducibility is emerging as the technology's main pitfalls. To examine some of the underlining factors leading to such discrepancies, we reasoned that perhaps there is an inherent difference in knockdown efficiency of the various RNAi technologies. For this purpose, we utilized the two most popular ones, chemically synthesized siRNA duplex and plasmid-based shRNA hairpin, in order to perform a head to head comparison. Using a previously developed gain-of-function assay probing modulators of the miRNA biogenesis pathway, we first executed on a siRNA screen against the Silencer Select V4.0 library (AMB) nominating 1,273, followed by an shRNA screen against the TRC1 library (TRC1) nominating 497 gene candidates. We observed a poor overlap of only 29 hits given that there are 15,068 overlapping genes between the two libraries; with DROSHA as the only common hit out of the seven known core miRNA biogenesis genes. Distinct genes interacting with the same biogenesis regulators were observed in both screens, with a dismal cross-network overlap of only 3 genes (DROSHA, TGFBR1, and DIS3). Taken together, our study demonstrates differential knockdown activities between the two technologies, possibly due to the inefficient intracellular processing and potential cell-type specificity determinants in generating intended targeting sequences for the plasmid-based shRNA hairpins; and suggests this observed inefficiency as potential culprit in addressing the lack of reproducibility.

Using Self-Organizing Map (SOM) and Support Vector Machine (SVM), four classification models were built to predict whether a compound is an active or weakly active inhibitor of Aurora B kinase. A dataset of 679 Aurora B kinase inhibitors was collected, and randomly split into a training set (278 active and 204 weakly active inhibitors) and a test set (109 active and 88 weakly active inhibitors). Based on 19 selected ADRIANA.Code descriptors and 135 MACCS fingerprints, all the four models showed a good prediction accuracy of over 87% on the test set. It benefited from the advantages of two different types of molecular descriptors in encoding structure information of compounds and characterizing the diversity of different inhibitors. Some molecular properties, such as hydrogen-bonding interactions and atom charge related descriptors were found to be important to the bioactivity of Aurora B kinase inhibitors.

Insomnia is one of the most common clinical problems being faced by people all over the world. It adversely affects the routine life of these patients giving rise to even other health issues like hypertension, diabetes, obesity, depression, heart attack, and stroke. Orexin receptor-1 (OX1R), a noteworthy drug target, when inhibited can promote sleepiness in people suffering from such conditions. OX1R is a G-protein coupled receptor which is conserved throughout the mammalian species and is located primarily in hypothalamus and locus coeruleus. The present study aims at identifying potent natural-origin inhibitors of OX1R capable of affecting the arousal and sleep pattern. In the present work, we have screened a large dataset of natural compounds against OX1R using high throughput screening and high precision docking approaches. Molecular dynamics simulations were carried out to study the dynamical behavior of the top scoring compound. We also provided mechanistic insights into the binding mode of action of this compound. The study provides evidence for consideration of this natural molecule as prospective lead in treatment of insomnia.

A one-pot, three-component reaction of an isatin and kojic acid with an active methylene compound such as ethyl cyanoacetate, methyl cyanoacetate and malononitrile in methanol using catalytic amount of DABCO to give 2'- amino-6'-(hydroxymethyl)-8'H-spiro[indoline-3,4'-pyrano[3,2-b]pyran]-2,8'-diones in good to excellent yields under reflux conditions, is described.

An artificial polypeptide receptor (APR) library was created by using the self-organization of N-lipidated peptides attached to cellulose via m-aminophenylamino-1,3,5-triazine. The response of the library was probed using a series of novel H3 receptor ligands. Since no guidelines on how to design an APRs selective vs certain receptor types exist, a diverse set of amino acids (Ala, Trp, Pro, Glu, His, Lys and Ser) were used and coupled with one of three gating fatty acids (palmitic, ricinoleic or capric). A competitive adsorption-desorption of an appropriate reporter dye was used for the indirect visualization of the interactions of guests with particular receptors. The resulted library response to individual inhibitors was then arranged in a matrix, preprocessed and analyzed using the principal component analysis (PCA) and partial least squares (PLS) method. The most important conclusion obtained from the PCA analysis is that the library differentiates the probed compounds according to the lipophilicity of the gating unit. The PC3 with a dominant absolute contribution of the receptors containing Glu allowed for the best separation of the ligands with respect to their activity. This conclusion is in agreement with the fact that Glu 206 is a genuine ligand counterpart in the natural histamine receptor.

An efficient synthesis of alkyl 4,5-dihydro-4-oxo-2-phenylpyrrolo[2,1-c][1,4]benzoxazine-3-carboxylates and alkyl 4,5-dihydro-4-oxo-2-phenylpyrrolo[1,2-a]quinoxaline-3-carboxylates is described. This involves a three-component reaction between o-aminophenols or o-phenylenediamine, acetylenic esters and β-nitrostyrene derivatives using p-toluene sulfonic acid as a catalyst.

Flavonoids, the most diverse class of plant secondary metabolites, exhibit high affinity toward the purified cytosolic NBD2(C-terminal nucleotide-binding domain) of P-glycoprotein (P-gp). To explore the affinity of flavonoids for P-gp, quantitative structure-activity relationships (QSARs) models were developed using back-propagation artificial neural networks (BPANN) and multiple linear regression (MLR). Molecular descriptors were calculated using PaDELDescriptor, and the number of descriptors was then reduced using a genetic algorithm (GA) and stepwise regression. The MLR (R2=0.855, q2=0.8138, Rext 2=0.6916), 14-3-1 BPANN (R2=0.8514, q2=0.7695, Rext 2=0.8142), 14-4-1 BPANN (R2=0.9199, q2=0.7733, Rext 2=0.8731), and 14-5-1 BPANN (R2=0.8660, q2=0.7432, Rext 2=0.8292) models all showed good robustness. While BPANN models exceeded significantly MLR in predictable performance for their flexible characters, could be used to predict the affinity of flavonoids for P-gp and applied in further drug screening.

The last few decades have seen the rise of widely-available proteomics tools. From new data acquisition devices, such as MALDI-MS and 2DE to new database searching softwares, these new products have paved the way for high throughput microbial proteomics (HTMP). These tools are enabling researchers to gain new insights into microbial metabolism, and are opening up new areas of study, such as protein-protein interactions (interactomics) discovery. Computer software is a key part of these emerging fields. This current review considers: 1) software tools for identifying the proteome, such as MASCOT or PDQuest, 2) online databases of proteomes, such as SWISS-PROT, Proteome Web, or the Proteomics Facility of the Pathogen Functional Genomics Resource Center, and 3) software tools for applying proteomic data, such as PSI-BLAST or VESPA. These tools allow for research in network biology, protein identification, functional annotation, target identification/validation, protein expression, protein structural analysis, metabolic pathway engineering and drug discovery.

In this paper, CuInS2 (CIS) nanoparticles were synthesized successfully via a new copper precursor [bis(acetylacetonato)copper(II)], [Cu(acac)2]; at room temperature by ultrasonic method. The effect of sulfur source, solvent, and reaction time was investigated on product morphology and particle size. A series of analyses was performed to characterize the CuInS2 microsphere including X-ray diffraction (XRD), scanning electron microscopy (SEM), transmission electron microscopy (TEM) and photoluminescence (PL) spectroscopy. CuInS2 nanoparticles were prepared and coated on FTO. Later, the coated FTO was sintered so that a compact and dense CuInS2 film was produced and measured for photovoltaic characteristics such as Voc, Jsc; and FF.